// Start training from the collected faces.
// The face recognition algorithm can be one of these and perhaps more, depending on your version of OpenCV, which must be atleast v2.4.1:
// "FaceRecognizer.Eigenfaces": Eigenfaces, also referred to as PCA (Turk and Pentland, 1991).
// "FaceRecognizer.Fisherfaces": Fisherfaces, also referred to as LDA (Belhumeur et al, 1997).
// "FaceRecognizer.LBPH": Local Binary Pattern Histograms (Ahonen et al, 2006).
public static BasicFaceRecognizer LearnCollectedFaces(List <Mat> preprocessedFaces, List <int> faceLabels, string facerecAlgorithm = "FaceRecognizer.Eigenfaces")
{
BasicFaceRecognizer model = null;
Debug.Log("Learning the collected faces using the [" + facerecAlgorithm + "] algorithm ...");
if (facerecAlgorithm == "FaceRecognizer.Fisherfaces")
{
model = FisherFaceRecognizer.create();
}
else if (facerecAlgorithm == "FaceRecognizer.Eigenfaces")
{
model = EigenFaceRecognizer.create();
}
if (model == null)
{
Debug.LogError("ERROR: The FaceRecognizer algorithm [" + facerecAlgorithm + "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer.");
//exit(1);
}
// Do the actual training from the collected faces. Might take several seconds or minutes depending on input!
MatOfInt labels = new MatOfInt();
labels.fromList(faceLabels);
model.train(preprocessedFaces, labels);
return(model);
}
private void Run()
{
List <Mat> images = new List <Mat> ();
List <int> labelsList = new List <int> ();
MatOfInt labels = new MatOfInt();
images.Add(Imgcodecs.imread(image_0_filepath, 0));
images.Add(Imgcodecs.imread(image_1_filepath, 0));
labelsList.Add(0);
labelsList.Add(1);
labels.fromList(labelsList);
Mat testSampleMat = Imgcodecs.imread(sample_image_filepath, 0);
int testSampleLabel = 0;
// foreach (Mat item in images) {
// Debug.Log ("images.ToString " + item.ToString ());
// }
// foreach (int item in labelsList) {
// Debug.Log ("labels.ToString " + item.ToString ());
// }
int[] predictedLabel = new int[1];
double[] predictedConfidence = new double[1];
BasicFaceRecognizer faceRecognizer = EigenFaceRecognizer.create();
faceRecognizer.train(images, labels);
faceRecognizer.predict(testSampleMat, predictedLabel, predictedConfidence);
Debug.Log("Predicted class: " + predictedLabel [0] + " / " + "Actual class: " + testSampleLabel);
Debug.Log("Confidence: " + predictedConfidence [0]);
Mat predictedMat = images [predictedLabel [0]];
Mat baseMat = new Mat(testSampleMat.rows(), predictedMat.cols() + testSampleMat.cols(), CvType.CV_8UC1);
predictedMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(0, 0, predictedMat.cols(), predictedMat.rows())));
testSampleMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(predictedMat.cols(), 0, testSampleMat.cols(), testSampleMat.rows())));
Imgproc.putText(baseMat, "Predicted", new Point(10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "Confidence:", new Point(5, 25), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, " " + predictedConfidence [0], new Point(5, 33), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "TestSample", new Point(predictedMat.cols() + 10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Texture2D texture = new Texture2D(baseMat.cols(), baseMat.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(baseMat, texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
}
